基于突触电导计算分离的一种新的时钟同步算法
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摘要
为降低生物神经网络仿真时间,提出一种基于突触电导计算分离的有效时钟同步算法。发现突触的仿真计算可分解为突触电导系数和突触电流两个独立计算部分;进一步引入"虚拟突触簇"数据结构存储突触电导系数序列,在每个仿真步根据突触前神经元放电状况单独计算突触电导系数并以循环数组结构保存,在计算以该神经元为突触前神经元的所有突触的突触电流时,只需从该数据结构中获取电导系数,从而大大减少了突触电导系数的重复计算。仿真结果表明了该算法的有效性。
In order to reduce the computing time when simulating the biologic neural network,an efficient clock-driven algorithm based on the separation of synaptic conductance computation is presented.It is found that the calculation of the synaptic state variables can be separated into two independent parts:one called conductance coefficient related with the pre-synaptic neuron,and the other called synaptic current.By introducing the data structure of the virtual synapse cluster to storing sequences of synaptic conductance coefficient,the former part can be calculated independently according to the spiking states of pre-synaptic neuron at each time step.When calculating the currents of all the synapses related with this pre-synaptic neuron,it is only need to calculate synaptic current by accessing the conductance coefficient from the virtual synapse cluster.Thus,the repetition of the calculation of the synaptic conductance coefficient is reduced.Simulation results validate the algorithm proposed in this paper.
In order to reduce the computing time when simulating the biologic neural network,an efficient clock-driven algorithm based on the separation of synaptic conductance computation is presented.It is found that the calculation of the synaptic state variables can be separated into two independent parts:one called conductance coefficient related with the pre-synaptic neuron,and the other called synaptic current.By introducing the data structure of the virtual synapse cluster to storing sequences of synaptic conductance coefficient,the former part can be calculated independently according to the spiking states of pre-synaptic neuron at each time step.When calculating the currents of all the synapses related with this pre-synaptic neuron,it is only need to calculate synaptic current by accessing the conductance coefficient from the virtual synapse cluster.Thus,the repetition of the calculation of the synaptic conductance coefficient is reduced.Simulation results validate the algorithm proposed in this paper.
引文
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[2]Mi?kowski M.Explanatory completeness and idealization in large brain simulations:a mechanistic perspective[J].Synthese(S0039-7857),2016,193(5):1457-1478.
[3]Carnevale N T,Hines M L.The NEURON book[M].Cambridge:Cambridge University Press,2006.
[4]Kim D,ParéD,Nair S S.Mechanisms contributing to the induction and storage of Pavlovian fear memories in the lateral amygdala[J].Learning&Memory(S1072-0502),2013,20(8):421-430.
[5]Basu I,Kudela P,Korzeniewska A,et al.A study of the dynamics of seizure propagation across micro domains in the vicinity of the seizure onset zone[J].Journal of Neural Engineering(S1741-2560),2015,12(4):046016.
[6]Jan M,Tomohiro S,Kenji D.The Mechanism of Saccade Motor Pattern Generation Investigated by a Large-Scale Spiking Neuron Model of the Superior Colliculus[J].Plos One(S1932-6203),2013,8(2):e57134.
[7]Markram H.The Blue Brain Project[J].Nature Rev Neurosci(S1471-003X),2006,7(2):153-160.
[8]D'Haene M,Schrauwen B,Van C J,et al.Accelerating event-driven simulation of spiking neurons with multiple synaptic time constants[J].Neural Computation(S0899-7667),2009,21(4):1068.
[9]Touboul J,Faugeras O,Rochel O.Event-driven mathematical framework for noisy integrate-and-fire neuron networks:spike trains statistics via stochastic calculus,network analysis inspired by queuing theory and an event-driven simulator[J].BMC Neuroscience(S1471-2202),2007,8(2):1-2.
[10]Reutimann J,Giugliano M,Fusi S.Event-driven Simulation of Spiking Neurons with Stochastic Dynamics[J].Neural Computation(S0899-7667),2003,15(4):811-830.
[11]Sacerdote L,Giraudo M T.Stochastic Integrate and Fire Models:A Review on Mathematical Methods and Their Applications[M]//Bachar M,Batzel J,Ditlevsen S.Stochastic Biomathematical Models:with Applications to Neuronal Modeling,Berlin,Heidelberg:Springer Berlin Heidelberg,2013:99-148.
[12]Dayan P,Abbott L F.Theoretical Neuroscience:Computational and Mathematical Modeling of Neural Systems[M].Cambridge:Massachusetts and London:England:The MIT Press,2005.